用图神经网络提升无人机与网络安全联动响应能力
Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict
- 构建基于图神经网络的攻防联动框架，实现结构化感知
- 检测率达94.2%，平均响应仅1.4秒，ROC-AUC达0.955
- 适合安全防护、智能无人机系统研发人员参考
物理网络系统带来了新型威胁与实时响应挑战。本研究探讨图神经网络（GNN）在包含网络入侵和无人飞行器（UAVs）的物理网络系统中，如何协同支持网络安全与无人机管理。通过构建图结构理解桥梁，提出一体化流程，使入侵检测系统能学习网络拓扑结构，识别恶意行为，并驱动无人机响应。基于仿真案例，模拟攻击以触发无人机反应，验证了基于图的学习在态势感知、集群协同与自适应机动方面的有效性。性能评估显示，该方法检测率为94.2%，平均受试者工作特征曲线下面积（ROC-AUC）为0.955，平均响应时间为1.4秒。对比实验表明，在相同条件下，所提出的GraphSAGE网络优于图卷积网络（GCNs）与图注意力网络（GATs）。结果证明，图神经网络可用于防范动态网络物理系统的入侵与响应。
原文摘要 · Abstract (English)
Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles (UAVs). By providing a bridge between structural understanding of graphical neural networks, this work has provided an integrated procedure that allows intrusion detection systems to educate on underlying network structures, identify malicious activity, and facilitates drone response measures. Based on an emulation-based case study, cyberattacks models were created to provoke the responses of the drones, which proved that graph-based learning can assist with the situational awareness, swarm coordination, and adaptive maneuver. According to the performance valuation, this method has a detection rate of 94.2, average area under the receiver operating characteristic (ROC) of 0.955 and an average response time of 1.4 seconds. Comparative experiments reveal that proposed GraphSAGE network is more effective than the Graphical Convolutional Networks (GCNs) and Graphical Attention Networks (GATs) in the identical situation. Such findings prove that graphical neural networks can be used to avert intrusion and response of dynamic cyber-physical systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。